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The extraction of individual reference strings from the reference section of scientific publications is an important step in the citation extraction pipeline. Current approaches divide this task into two steps by first detecting the reference section areas and then grouping the text lines in such areas into reference strings. We propose a classification model that considers every line in a publication as a potential part of a reference string. By applying line-based conditional random fields rather than constructing the graphical model based on individual words, dependencies and patterns that are typical in reference sections provide strong features while the overall complexity of the model is reduced. We evaluated our novel approach RefExt against various state-of the-art tools (CERMINE, GROBID, and ParsCit) and a gold standard which consists of 100 German language full text publications from the social sciences. The evaluation demonstrates that we are able to outperform state-of-the-art tools which rely on the identification of reference section areas.